LearningCoefficient-RLCT-ofNMF
# LearningCoefficient-RLCT-ofNMF
Numerical Experiment MATLAB Codes for calculating real log canonical threshold (Bayesian generalization error) for NMF.
This experiment had been carried out for \[Hayashi, 2017b\].
## Research
See http://nhayashi.main.jp/publications-e.html
## References
* \[Aoyagi, 2005\]: Miki Aoyagi. Sumio Watanabe. "Stochastic Complexities of Reduced Rank Regression in Bayesian Estimation", Neural Networks, 2005, No. 18, pp.924-933.
* \[Hayashi, 2017a\]: Naoki Hayashi, Sumio Watanabe. "Upper Bound of Bayesian Generalization Error in Non-Negative Matrix Factorization", Neurocomputing, Volume 266C, 29 November 2017, pp.21-28. doi: 10.1016/j.neucom.2017.04.068. (2016/12/13 submitted. 2017/8/7 published on web).
* \[Hayashi, 2017b\]: Naoki Hayashi, Sumio Watanabe. "Tighter Upper Bound of Real Log Canonical Threshold of Non-negative Matrix Factorization and its Application to Bayesian Inference." 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Honolulu, Hawaii, USA. Nov. 27 - Dec 1, 2017. (2017/11/28).
Cite As
Naoki Hayashi (2024). LearningCoefficient-RLCT-ofNMF (https://github.com/chijan-nh/LearningCoefficient-RLCT-ofNMF), GitHub. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
- Computational Finance > Econometrics Toolbox > Time Series Regression Models > Bayesian Linear Regression Models >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
Versions that use the GitHub default branch cannot be downloaded
Version | Published | Release Notes | |
---|---|---|---|
1.0.0 |
|